With　the　rapid　development　of　information　technology　and　biomedical　engineering,　people　can　get　more　and　more　information.　At　the　same　time,　they　begin　to　study　how　to　apply　the　advanced　technology　in　biomedical　information.　The　main　research　of　this　paper　is　to　optimize　the　machine　learning　method　by　particle　swarm　optimization　(PSO)　and　apply　it　in　the　classification　of　biomedical　data.　In　order　to　improve　the　performance　of　the　classification　model,　we　compared　the　different　inertia　weight　strategies　and　mutation　strategies　and　their　combinations　with　PSO,　and　obtained　the　best　inertia　weight　strategy　without　mutation,　the　best　mutation　strategy　without　inertia　weight　and　the　best　combination　of　the　two.　Then,　we　used　the　three　PSO　algorithms　to　optimize　the　parameters　of　support　vector　machine　in　the　classification　of　biomedical　data.　We　found　that　the　PSO　algorithm　with　the　combination　of　inertia　weight　and　mutation　strategy　and　the　inertia　weight　strategy　that　we　proposed　could　improve　the　classification　accuracy.　This　study　has　an　important　reference　value　for　the　prediction　of　clinical　diseases.